The Missing Dead: The Problem of Case Ascertainment in the Assessment of Trauma Center Performance
Bibliographic record
Abstract
BACKGROUND: If there are systematic differences in the types of patients captured in registries, then differences in outcomes in centers might be related not to differences in the practice of care, but differences in registry inclusion criteria. We set out to evaluate the effect of variable case ascertainment of dead on arrivals on external benchmarking of risk-adjusted mortality using a form of sensitivity analysis. METHODS: We used data from the National Trauma Data Bank to look for indirect evidence of systematic differences in case ascertainment. We evaluated whether there was any relationship between fewer than expected early (< or = 24 hours) deaths and overall risk-adjusted mortality. Fewer than expected early deaths were estimated through the W statistic and through an adjusted ratio of early to late (E/L) deaths. E/L ratios were assessed due to the potential correlation between performance and absolute number of early deaths as assessed by the W statistic. RESULTS: We estimate that as many as 47% of all deaths might be missing due to problems with case ascertainment. Centers with unexpectedly few early deaths (W statistic) were consistently high performing centers with a lower than expected overall mortality. More importantly, there was no relationship between the E/L death ratio and overall risk-adjusted mortality. CONCLUSIONS: Variable case ascertainment of dead on arrivals does not affect the ability to assess performance. Given that our approach has several assumptions, it is critically important that external validation of trauma registries be performed. If centers are to be judged through the quality of their data, then it is incumbent to first assure that data quality meets expectations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.522 | 0.805 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".